






"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Any      =  (DPMSolverSinglestepScheduler,)
					lowercase_    :				Any      =  (("""num_inference_steps""", 25),)
					def        UpperCAmelCase__ (							self							:	Dict     , **UpperCAmelCase							:	Union[str, Any]   )	->       int:
											lowerCAmelCase   :Union[str, Any]						=			{
											    'num_train_timesteps': 1000,
											    'beta_start': 0.0_0_0_1,
											    'beta_end': 0.0_2,
											    'beta_schedule': 'linear',
											    'solver_order': 2,
											    'prediction_type': 'epsilon',
											    'thresholding': False,
											    'sample_max_value': 1.0,
											    'algorithm_type': 'dpmsolver++',
											    'solver_type': 'midpoint',
											    'lambda_min_clipped': -float('inf'   ),
											    'variance_type': None,
											}

											config.update(**UpperCAmelCase   )
											return config
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Union[str, Any]=0     , **UpperCAmelCase							:	Optional[Any]   )	->       Union[str, Any]:
											lowerCAmelCase   :Optional[Any]						=			dict(self.forward_default_kwargs   )
											lowerCAmelCase   :Any						=			kwargs.pop('num_inference_steps'     , UpperCAmelCase   )
											lowerCAmelCase   :int						=			self.dummy_sample
											lowerCAmelCase   :str						=			0.1 * sample
											lowerCAmelCase   :List[Any]						=			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

											for scheduler_class in self.scheduler_classes:
																	lowerCAmelCase   :int						=			self.get_scheduler_config(**UpperCAmelCase   )
																	lowerCAmelCase   :Union[str, Any]						=			scheduler_class(**UpperCAmelCase   )
																	scheduler.set_timesteps(UpperCAmelCase   )
																	# copy over dummy past residuals
																	lowerCAmelCase   :Tuple						=			dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(UpperCAmelCase   )
																							lowerCAmelCase   :List[Any]						=			scheduler_class.from_pretrained(UpperCAmelCase   )
																							new_scheduler.set_timesteps(UpperCAmelCase   )
																							# copy over dummy past residuals
																							lowerCAmelCase   :Tuple						=			dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowerCAmelCase	, lowerCAmelCase   :Dict						=			sample, sample
																	for t in range(UpperCAmelCase     , time_step + scheduler.config.solver_order + 1   ):
																							lowerCAmelCase   :Optional[int]						=			scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , **UpperCAmelCase   ).prev_sample
																							lowerCAmelCase   :Union[str, Any]						=			new_scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , **UpperCAmelCase   ).prev_sample

																							assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Tuple:
											pass
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	Optional[Any]=0     , **UpperCAmelCase							:	Optional[int]   )	->       Any:
											lowerCAmelCase   :Tuple						=			dict(self.forward_default_kwargs   )
											lowerCAmelCase   :Optional[Any]						=			kwargs.pop('num_inference_steps'     , UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			self.dummy_sample
											lowerCAmelCase   :List[Any]						=			0.1 * sample
											lowerCAmelCase   :List[Any]						=			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

											for scheduler_class in self.scheduler_classes:
																	lowerCAmelCase   :Optional[int]						=			self.get_scheduler_config()
																	lowerCAmelCase   :List[str]						=			scheduler_class(**UpperCAmelCase   )
																	scheduler.set_timesteps(UpperCAmelCase   )

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowerCAmelCase   :List[Any]						=			dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(UpperCAmelCase   )
																							lowerCAmelCase   :Tuple						=			scheduler_class.from_pretrained(UpperCAmelCase   )
																							# copy over dummy past residuals
																							new_scheduler.set_timesteps(UpperCAmelCase   )

																							# copy over dummy past residual (must be after setting timesteps)
																							lowerCAmelCase   :Optional[Any]						=			dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowerCAmelCase   :str						=			scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , **UpperCAmelCase   ).prev_sample
																	lowerCAmelCase   :int						=			new_scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , **UpperCAmelCase   ).prev_sample

																	assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Dict=None     , **UpperCAmelCase							:	str   )	->       List[str]:
											if scheduler is None:
																	lowerCAmelCase   :Any						=			self.scheduler_classes[0]
																	lowerCAmelCase   :Dict						=			self.get_scheduler_config(**UpperCAmelCase   )
																	lowerCAmelCase   :List[Any]						=			scheduler_class(**UpperCAmelCase   )

											lowerCAmelCase   :List[str]						=			self.scheduler_classes[0]
											lowerCAmelCase   :Optional[Any]						=			self.get_scheduler_config(**UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			scheduler_class(**UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			10
											lowerCAmelCase   :Optional[Any]						=			self.dummy_model()
											lowerCAmelCase   :Optional[Any]						=			self.dummy_sample_deter
											scheduler.set_timesteps(UpperCAmelCase   )

											for i, t in enumerate(scheduler.timesteps   ):
																	lowerCAmelCase   :List[str]						=			model(UpperCAmelCase     , UpperCAmelCase   )
																	lowerCAmelCase   :int						=			scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											return sample
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       List[str]:
											lowerCAmelCase   :Union[str, Any]						=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()   )
											lowerCAmelCase   :str						=			50
											lowerCAmelCase   :int						=			self.dummy_model()
											lowerCAmelCase   :Union[str, Any]						=			self.dummy_sample_deter
											scheduler.set_timesteps(UpperCAmelCase   )

											# make sure that the first t is uneven
											for i, t in enumerate(scheduler.timesteps[3:]   ):
																	lowerCAmelCase   :List[Any]						=			model(UpperCAmelCase     , UpperCAmelCase   )
																	lowerCAmelCase   :List[Any]						=			scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :List[Any]						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.2_5_7_4   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict:
											for timesteps in [25, 50, 100, 999, 1000]:
																	self.check_over_configs(num_train_timesteps=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       str:
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											lowerCAmelCase   :Union[str, Any]						=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()   )
											lowerCAmelCase   :str						=			self.full_loop(scheduler=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.2_7_9_1   ) < 1e-3

											lowerCAmelCase   :Optional[int]						=			DEISMultistepScheduler.from_config(scheduler.config   )
											lowerCAmelCase   :int						=			DPMSolverMultistepScheduler.from_config(scheduler.config   )
											lowerCAmelCase   :str						=			UniPCMultistepScheduler.from_config(scheduler.config   )
											lowerCAmelCase   :List[Any]						=			DPMSolverSinglestepScheduler.from_config(scheduler.config   )

											lowerCAmelCase   :Union[str, Any]						=			self.full_loop(scheduler=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.2_7_9_1   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	int   )	->       Union[str, Any]:
											self.check_over_configs(thresholding=UpperCAmelCase   )
											for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																							for threshold in [0.5, 1.0, 2.0]:
																													for prediction_type in ["epsilon", "sample"]:
																																			self.check_over_configs(
																																			    thresholding=UpperCAmelCase     , prediction_type=UpperCAmelCase     , sample_max_value=UpperCAmelCase     , algorithm_type='dpmsolver++'     , solver_order=UpperCAmelCase     , solver_type=UpperCAmelCase     , )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[Any]:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Union[str, Any]:
											for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																							for order in [1, 2, 3]:
																													for prediction_type in ["epsilon", "sample"]:
																																			self.check_over_configs(
																																			    solver_order=UpperCAmelCase     , solver_type=UpperCAmelCase     , prediction_type=UpperCAmelCase     , algorithm_type=UpperCAmelCase     , )
																																			lowerCAmelCase   :Optional[Any]						=			self.full_loop(
																																			    solver_order=UpperCAmelCase     , solver_type=UpperCAmelCase     , prediction_type=UpperCAmelCase     , algorithm_type=UpperCAmelCase     , )
																																			assert not torch.isnan(UpperCAmelCase   ).any(), "Samples have nan numbers"
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Union[str, Any]:
											self.check_over_configs(lower_order_final=UpperCAmelCase   )
											self.check_over_configs(lower_order_final=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       str:
											self.check_over_configs(lambda_min_clipped=-float('inf'   )   )
											self.check_over_configs(lambda_min_clipped=-5.1   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       int:
											self.check_over_configs(variance_type=UpperCAmelCase   )
											self.check_over_configs(variance_type='learned_range'   )
					def        UpperCAmelCase__ (							self							:	int   )	->       int:
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																	self.check_over_forward(num_inference_steps=UpperCAmelCase     , time_step=0   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       int:
											lowerCAmelCase   :Optional[Any]						=			self.full_loop()
											lowerCAmelCase   :Any						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.2_7_9_1   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[str]:
											lowerCAmelCase   :Optional[Any]						=			self.full_loop(use_karras_sigmas=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.2_2_4_8   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			self.full_loop(prediction_type='v_prediction'   )
											lowerCAmelCase   :List[str]						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.1_4_5_3   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Tuple:
											lowerCAmelCase   :Dict						=			self.full_loop(prediction_type='v_prediction'     , use_karras_sigmas=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			torch.mean(torch.abs(UpperCAmelCase   )   )

											assert abs(result_mean.item() - 0.0_6_4_9   ) < 1e-3
					def        UpperCAmelCase__ (							self							:	int   )	->       str:
											lowerCAmelCase   :Tuple						=			self.scheduler_classes[0]
											lowerCAmelCase   :Optional[Any]						=			self.get_scheduler_config(thresholding=UpperCAmelCase     , dynamic_thresholding_ratio=0   )
											lowerCAmelCase   :str						=			scheduler_class(**UpperCAmelCase   )

											lowerCAmelCase   :Optional[int]						=			10
											lowerCAmelCase   :Optional[int]						=			self.dummy_model()
											lowerCAmelCase   :Optional[int]						=			self.dummy_sample_deter.half()
											scheduler.set_timesteps(UpperCAmelCase   )

											for i, t in enumerate(scheduler.timesteps   ):
																	lowerCAmelCase   :Tuple						=			model(UpperCAmelCase     , UpperCAmelCase   )
																	lowerCAmelCase   :Any						=			scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											assert sample.dtype == torch.floataa







"""simple docstring"""


import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE   =   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')




@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Optional[int]      =  SpeechTaTokenizer
					lowercase_    :				Dict      =  False
					lowercase_    :				Any      =  True
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[Any]:
											super().setUp()

											# We have a SentencePiece fixture for testing
											lowerCAmelCase   :Union[str, Any]						=			SpeechTaTokenizer(UpperCAmelCase   )

											lowerCAmelCase   :Any						=			AddedToken('<mask>'     , lstrip=UpperCAmelCase     , rstrip=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			mask_token
											tokenizer.add_special_tokens({'mask_token': mask_token}   )
											tokenizer.add_tokens(['<ctc_blank>']   )

											tokenizer.save_pretrained(self.tmpdirname   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Any   )	->       Union[str, Any]:
											lowerCAmelCase   :Tuple						=			'this is a test'
											lowerCAmelCase   :Union[str, Any]						=			'this is a test'
											return input_text, output_text
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Any=False     , UpperCAmelCase							:	List[Any]=20     , UpperCAmelCase							:	str=5   )	->       Optional[Any]:
											lowerCAmelCase	, lowerCAmelCase   :Dict						=			self.get_input_output_texts(UpperCAmelCase   )
											lowerCAmelCase   :str						=			tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			tokenizer.decode(UpperCAmelCase     , clean_up_tokenization_spaces=UpperCAmelCase   )
											return text, ids
					def        UpperCAmelCase__ (							self							:	Any   )	->       Optional[Any]:
											lowerCAmelCase   :List[Any]						=			'<pad>'
											lowerCAmelCase   :Any						=			1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase   )     , UpperCAmelCase   )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase   )     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Any:
											lowerCAmelCase   :Dict						=			list(self.get_tokenizer().get_vocab().keys()   )

											self.assertEqual(vocab_keys[0]     , '<s>'   )
											self.assertEqual(vocab_keys[1]     , '<pad>'   )
											self.assertEqual(vocab_keys[-4]     , 'œ'   )
											self.assertEqual(vocab_keys[-2]     , '<mask>'   )
											self.assertEqual(vocab_keys[-1]     , '<ctc_blank>'   )
											self.assertEqual(len(UpperCAmelCase   )     , 81   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Union[str, Any]:
											self.assertEqual(self.get_tokenizer().vocab_size     , 79   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       List[str]:
											lowerCAmelCase   :Any						=			self.get_tokenizers(do_lower_case=UpperCAmelCase   )
											for tokenizer in tokenizers:
																	with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
																							lowerCAmelCase   :Optional[int]						=			tokenizer.vocab_size
																							lowerCAmelCase   :Union[str, Any]						=			len(UpperCAmelCase   )

																							self.assertNotEqual(UpperCAmelCase     , 0   )

																							# We usually have added tokens from the start in tests because our vocab fixtures are
																							# smaller than the original vocabs - let's not assert this
																							# self.assertEqual(vocab_size, all_size)

																							lowerCAmelCase   :Optional[Any]						=			['aaaaa bbbbbb', 'cccccccccdddddddd']
																							lowerCAmelCase   :Union[str, Any]						=			tokenizer.add_tokens(UpperCAmelCase   )
																							lowerCAmelCase   :str						=			tokenizer.vocab_size
																							lowerCAmelCase   :List[Any]						=			len(UpperCAmelCase   )

																							self.assertNotEqual(UpperCAmelCase     , 0   )
																							self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
																							self.assertEqual(UpperCAmelCase     , len(UpperCAmelCase   )   )
																							self.assertEqual(UpperCAmelCase     , all_size + len(UpperCAmelCase   )   )

																							lowerCAmelCase   :int						=			tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'     , add_special_tokens=UpperCAmelCase   )

																							self.assertGreaterEqual(len(UpperCAmelCase   )     , 4   )
																							self.assertGreater(tokens[0]     , tokenizer.vocab_size - 1   )
																							self.assertGreater(tokens[-3]     , tokenizer.vocab_size - 1   )

																							lowerCAmelCase   :List[Any]						=			{'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
																							lowerCAmelCase   :str						=			tokenizer.add_special_tokens(UpperCAmelCase   )
																							lowerCAmelCase   :Optional[Any]						=			tokenizer.vocab_size
																							lowerCAmelCase   :Tuple						=			len(UpperCAmelCase   )

																							self.assertNotEqual(UpperCAmelCase     , 0   )
																							self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
																							self.assertEqual(UpperCAmelCase     , len(UpperCAmelCase   )   )
																							self.assertEqual(UpperCAmelCase     , all_size_a + len(UpperCAmelCase   )   )

																							lowerCAmelCase   :List[str]						=			tokenizer.encode(
																							    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'     , add_special_tokens=UpperCAmelCase   )

																							self.assertGreaterEqual(len(UpperCAmelCase   )     , 6   )
																							self.assertGreater(tokens[0]     , tokenizer.vocab_size - 1   )
																							self.assertGreater(tokens[0]     , tokens[1]   )
																							self.assertGreater(tokens[-3]     , tokenizer.vocab_size - 1   )
																							self.assertGreater(tokens[-3]     , tokens[-4]   )
																							self.assertEqual(tokens[0]     , tokenizer.eos_token_id   )
																							self.assertEqual(tokens[-3]     , tokenizer.pad_token_id   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[Any]:
											pass
					def        UpperCAmelCase__ (							self							:	str   )	->       int:
											pass
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       List[Any]:
											lowerCAmelCase   :Optional[int]						=			self.get_tokenizer()

											lowerCAmelCase   :List[str]						=			tokenizer.tokenize('This is a test'   )
											# fmt: off
											self.assertListEqual(UpperCAmelCase     , [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't']   )
											# fmt: on

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(UpperCAmelCase   )     , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]     , )

											lowerCAmelCase   :List[str]						=			tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
											self.assertListEqual(
											    UpperCAmelCase     , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']   )

											lowerCAmelCase   :Optional[int]						=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
											# fmt: off
											self.assertListEqual(UpperCAmelCase     , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]   )
											# fmt: on

											lowerCAmelCase   :int						=			tokenizer.convert_ids_to_tokens(UpperCAmelCase   )
											self.assertListEqual(
											    UpperCAmelCase     , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']   )
					@slow
					def        UpperCAmelCase__ (							self							:	str   )	->       Tuple:
											# Use custom sequence because this tokenizer does not handle numbers.
											lowerCAmelCase   :Any						=			[
											    'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
											    'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
											    'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
											    'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
											    'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
											    'conditioning on both left and right context in all layers.',
											    'The quick brown fox jumps over the lazy dog.',
											]

											# fmt: off
											lowerCAmelCase   :List[str]						=			{
											    'input_ids': [
											        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
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											    ],
											    'attention_mask': [
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											    ]
											}
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCAmelCase     , model_name='microsoft/speecht5_asr'     , revision='c5ef64c71905caeccde0e4462ef3f9077224c524'     , sequences=UpperCAmelCase     , )